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IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Indexing: Efficient 3-D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
A Bayesian approach to model matching with geometric hashing
Computer Vision and Image Understanding
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Hashing: An Overview
IEEE Computational Science & Engineering
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Object Recognition from Local Scale-Invariant Features
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Mean Shift Based Clustering in High Dimensions: A Texture Classification Example
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Many-to-many graph matching via metric embedding
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Shapeme Histogram Projection and Matching for Partial Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid Object Indexing Using Locality Sensitive Hashing and Joint 3D-Signature Space Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A planar-reflective symmetry transform for 3D shapes
ACM SIGGRAPH 2006 Papers
Reassembling fractured objects by geometric matching
ACM SIGGRAPH 2006 Papers
Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive regions of 3D surfaces
ACM Transactions on Graphics (TOG)
Partial matching of 3D shapes with priority-driven search
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
Three-Dimensional Tracking at Micro-scale Using a Single Optical Microscope
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part II
Real-time Object Recognition in Sparse Range Images Using Error Surface Embedding
International Journal of Computer Vision
An efficient RANSAC for 3D object recognition in noisy and occluded scenes
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Exploiting model similarity for indexing and matching to a large model database
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Learning the compositional structure of man-made objects for 3D shape retrieval
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
Tactile sensors based object recognition and 6d pose estimation
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part III
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This paper proposes a joint feature-based model indexing and geometric constraint based alignment pipeline for efficient and accurate recognition of 3D objects from a large model database. Traditional approaches either first prune the model database using indexing without geometric alignment or directly perform recognition based alignment. The indexing based pruning methods without geometric constraints can miss the correct models under imperfections such as noise, clutter and obscurations. Alignment based verification methods have to linearly verify each model in the database and hence do not scale up. The proposed techniques use spin images as semi-local shape descriptors and Locality-Sensitive Hashing (LSH) to index into a joint spin image database for all the models. The indexed models represented in the pruned set are further pruned using progressively complex geometric constraints. A simple geometric configuration of multiple spin images, for instance a doublet, is first used to check for geometric consistency. Subsequently, full Euclidean geometric constraints are applied using RANSAC-based techniques on the pruned spin images and the models to verify specific object identity. As a result, the combined indexing and geometric alignment based pipeline is able to focus on matching the most promising models, and generate far less pose hypotheses while maintaining the same level of performance as the sequential alignment based recognition. Furthermore, compared to geometric indexing techniques like Geometric Hashing, the construction time and storage complexity for the proposed technique remains linear in the number of features rather than higher order polynomial. Experiments on a 56 3D model database show promising Results.